{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Time Series Prediction\n",
    "\n",
    "**Objectives**\n",
    " 1. Build a linear, DNN and CNN model in Keras.\n",
    " 2. Build a simple RNN model and a multi-layer RNN model in Keras.\n",
    " \n",
    "In this lab we will with a linear, DNN and CNN model \n",
    "\n",
    "Since the features of our model are sequential in nature, we'll next look at how to build various RNN models in Keras. We'll start with a simple RNN model and then see how to create a multi-layer RNN in Keras.\n",
    "\n",
    "We will be exploring a lot of different model types in this notebook.\n"
   ]
  },

  {
    "cell_type": "code",
    "metadata": {
      "id": "Nny3m465gKkY",
      "colab_type": "code",
      "colab": {}
    },
    "source": [
      "!sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst"
    ],
    "execution_count": null,
    "outputs": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
  "Collecting google-cloud-bigquery==1.25.0\n",
  "Downloading google_cloud_bigquery-1.25.0-py2.py3-none-any.whl (169 kB)\n",
     "|████████████████████████████████| 169 kB 4.7 MB/s eta 0:00:01\n",

  "Requirement already satisfied:  six in /home/jupyter/.local/lib/python3.7/site-packages (from google-cloud-bigquery==1.25.0)\n",
  "Requirement already satisfied: google-auth in /usr/local/lib/python3.7/site-packages (from google-cloud-bigquery==1.25.0)\n",
  "Requirement already satisfied: google-resumable-media in /usr/local/lib/python3.7/dist-packages (from google-cloud-bigquery==1.25.0)\n",
  "Requirement already satisfied: google-cloud-core in /usr/local/lib/python3.7/dist-packages (from google-cloud-bigquery==1.25.0)\n",
  "Requirement already satisfied: protobuf in /usr/local/lib/python3.7/dist-packages (from google-cloud-bigquery==1.25.0)\n",
  "Requirement already satisfied: google-api-core in /usr/local/lib/python3.7/dist-packages (from google-cloud-bigquery==1.25.0)\n",
  "Requirement already satisfied: cachetools in /usr/local/lib/python3.7/dist-packages(from google-cloud-bigquery==1.25.0)\n",
  "Requirement already satisfied: rsa in /usr/local/lib/python3.7/dist-packages (from google-cloud-bigquery==1.25.0)\n",
  "Requirement already satisfied: pyasn1-modules in /usr/local/lib/python3.7/dist-packages (from google-cloud-bigquery==1.25.0)\n",
  "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from google-cloud-bigquery==1.25.0)\n",
  "Requirement already satisfied: googleapis-common-protos in /usr/local/lib/python3.7/dist-packages (from google-cloud-bigquery==1.25.0)\n",
  "Requirement already satisfied: pyasn1 in /usr/local/lib/python3.7/dist-packages (from google-cloud-bigquery==1.25.0)\n",
  "Requirement already satisfied: certifi in /usr/local/lib/python3.7/dist-packages (from google-cloud-bigquery==1.25.0)\n",
  "Installing collected packages: google-resumable-media, google-cloud-bigquery\n",
  "\u001b[33mWARNING: You are using pip version 20.1; however, version 20.2.3 is available."
     ]
    }
   ],
   "source": [
    "!pip install --user google-cloud-bigquery==1.25.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**: Restart your kernel to use updated packages."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kindly ignore the deprecation warnings and incompatibility errors related to google-cloud-storage."
   ]
  },

  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load necessary libraries and set up environment variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "PROJECT = \"your-gcp-project-here\" # REPLACE WITH YOUR PROJECT NAME\n",
    "BUCKET = \"your-gcp-bucket-here\" # REPLACE WITH YOUR BUCKET\n",
    "REGION = \"us-central1\" # REPLACE WITH YOUR BUCKET REGION e.g. us-central1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "%env \n",
    "PROJECT = PROJECT\n",
    "BUCKET = BUCKET\n",
    "REGION = REGION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "\n",
    "from google.cloud import bigquery\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import (Dense, DenseFeatures,\n",
    "                                     Conv1D, MaxPool1D,\n",
    "                                     Reshape, RNN,\n",
    "                                     LSTM, GRU, Bidirectional)\n",
    "from tensorflow.keras.callbacks import TensorBoard, ModelCheckpoint\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "\n",
    "# To plot pretty figures\n",
    "%matplotlib inline\n",
    "mpl.rc('axes', labelsize=14)\n",
    "mpl.rc('xtick', labelsize=12)\n",
    "mpl.rc('ytick', labelsize=12)\n",
    "\n",
    "# For reproducible results.\n",
    "from numpy.random import seed\n",
    "seed(1)\n",
    "tf.random.set_seed(2)"
   ]
  },
    {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explore time series data\n",
    "\n",
    "We'll start by pulling a small sample of the time series data from Big Query and write some helper functions to clean up the data for modeling. We'll use the data from the `percent_change_sp500` table in BigQuery. The `close_values_prior_260` column contains the close values for any given stock for the previous 260 days. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 76 ms, sys: 20 ms, total: 96 ms\n",
      "Wall time: 1.59 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "bq = bigquery.Client(project=PROJECT)\n",
    "\n",
    "bq_query = '''\n",
    "#standardSQL\n",
    "SELECT\n",
    "  symbol,\n",
    "  Date,\n",
    "  direction,\n",
    "  close_values_prior_260\n",
    "FROM\n",
    "  `stock_market.eps_percent_change_sp500`\n",
    "LIMIT\n",
    "  100\n",
    "'''\n"
  ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The function `clean_data` below does three things:\n",
    " 1. First, we'll remove any inf or NA values\n",
    " 2. Next, we parse the `Date` field to read it as a string.\n",
    " 3. Lastly, we convert the label `direction` into a numeric quantity, mapping 'DOWN' to 0, 'STAY' to 1 and 'UP' to 2. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_data(input_df):\n",
    "    \"\"\"Cleans data to prepare for training.\n",
    "\n",
    "    Args:\n",
    "        input_df: Pandas dataframe.\n",
    "    Returns:\n",
    "        Pandas dataframe.\n",
    "    \"\"\"\n",
    "    df = input_df.copy()\n",
    "\n",
    "    # Remove inf/na values.\n",
    "    real_valued_rows = ~(df == np.inf).max(axis=1)\n",
    "    df = df[real_valued_rows].dropna()\n",
    "\n",
    "    # TF doesn't accept datetimes in DataFrame.\n",
    "    df['Date'] = pd.to_datetime(df['Date'], errors='coerce')\n",
    "    df['Date'] = df['Date'].dt.strftime('%Y-%m-%d')\n",
    "\n",
    "    # TF requires numeric label.\n",
    "    df['direction_numeric'] = df['direction'].apply(lambda x: {'DOWN': 0,\n",
    "                                                               'STAY': 1,\n",
    "                                                               'UP': 2}[x])\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read data and preprocessing\n",
    "\n",
    "Before we begin modeling, we'll preprocess our features by scaling to the z-score. This will ensure that the range of the feature values being fed to the model are comparable and should help with convergence during gradient descent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "STOCK_HISTORY_COLUMN = 'close_values_prior_260'\n",
    "COL_NAMES = ['day_' + str(day) for day in range(0, 260)]\n",
    "LABEL = 'direction_numeric'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "def _scale_features(df):\n",
    "    \"\"\"z-scale feature columns of Pandas dataframe.\n",
    "\n",
    "    Args:\n",
    "        features: Pandas dataframe.\n",
    "    Returns:\n",
    "        Pandas dataframe with each column standardized according to the\n",
    "        values in that column.\n",
    "    \"\"\"\n",
    "    avg = df.mean()\n",
    "    std = df.std()\n",
    "    return (df - avg) / std\n",
    "\n",
    "\n",
    "def create_features(df, label_name):\n",
    "    \"\"\"Create modeling features and label from Pandas dataframe.\n",
    "\n",
    "    Args:\n",
    "        df: Pandas dataframe.\n",
    "        label_name: str, the column name of the label.\n",
    "    Returns:\n",
    "        Pandas dataframe\n",
    "    \"\"\"\n",
    "    # Expand 1 column containing a list of close prices to 260 columns.\n",
    "    time_series_features = df[STOCK_HISTORY_COLUMN].apply(pd.Series)\n",
    "\n",
    "    # Rename columns.\n",
    "    time_series_features.columns = COL_NAMES\n",
    "    time_series_features = _scale_features(time_series_features)\n",
    "\n",
    "    # Concat time series features with static features and label.\n",
    "    label_column = df[LABEL]\n",
    "\n",
    "    return pd.concat([time_series_features,\n",
    "                      label_column], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make train-eval-test split\n",
    "\n",
    "Next, we'll make repeatable splits for our train/validation/test datasets and save these datasets to local csv files. The query below will take a subsample of the entire dataset and then create a 70-15-15 split for the train/validation/test sets. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [],
   "source": [
    "def _create_split(phase):\n",
    "    \"\"\"Create string to produce train/valid/test splits for a SQL query.\n",
    "\n",
    "    Args:\n",
    "        phase: str, either TRAIN, VALID, or TEST.\n",
    "    Returns:\n",
    "        String.\n",
    "    \"\"\"\n",
    "    floor, ceiling = '2002-11-01', '2010-07-01'\n",
    "    if phase == 'VALID':\n",
    "        floor, ceiling = '2010-07-01', '2011-09-01'\n",
    "    elif phase == 'TEST':\n",
    "        floor, ceiling = '2011-09-01', '2012-11-30'\n",
    "    return '''\n",
    "    WHERE Date >= '{0}'\n",
    "    AND Date < '{1}'\n",
    "    '''.format(floor, ceiling)\n",
    "\n",
    "\n",
    "def create_query(phase):\n",
    "    \"\"\"Create SQL query to create train/valid/test splits on subsample.\n",
    "\n",
    "    Args:\n",
    "        phase: str, either TRAIN, VALID, or TEST.\n",
    "        sample_size: str, amount of data to take for subsample.\n",
    "    Returns:\n",
    "        String.\n",
    "    \"\"\"\n",
    "    basequery = \"\"\"\n",
    "    #standardSQL\n",
    "    SELECT\n",
    "      symbol,\n",
    "      Date,\n",
    "      direction,\n",
    "      close_values_prior_260\n",
    "    FROM\n",
    "      `stock_market.eps_percent_change_sp500`\n",
    "    \"\"\"\n",
    "    \n",
    "    return basequery + _create_split(phase)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modeling\n",
    "\n",
    "For experimentation purposes, we'll train various models using data we can fit in memory using the `.csv` files we created above. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [],
   "source": [
    "N_TIME_STEPS = 260\n",
    "N_LABELS = 3\n",
    "\n",
    "Xtrain = pd.read_csv('stock-train.csv')\n",
    "Xvalid = pd.read_csv('stock-valid.csv')\n",
    "\n",
    "ytrain = Xtrain.pop(LABEL)\n",
    "yvalid  = Xvalid.pop(LABEL)\n",
    "\n",
    "ytrain_categorical = to_categorical(ytrain.values)\n",
    "yvalid_categorical = to_categorical(yvalid.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To monitor training progress and compare evaluation metrics for different models, we'll use the function below to plot metrics captured from the training job such as training and validation loss or accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_curves(train_data, val_data, label='Accuracy'):\n",
    "    \"\"\"Plot training and validation metrics on single axis.\n",
    "\n",
    "    Args:\n",
    "        train_data: list, metrics obtrained from training data.\n",
    "        val_data: list, metrics obtained from validation data.\n",
    "        label: str, title and label for plot.\n",
    "    Returns:\n",
    "        Matplotlib plot.\n",
    "    \"\"\"\n",
    "    plt.plot(np.arange(len(train_data)) + 0.5,\n",
    "             train_data,\n",
    "             \"b.-\", label=\"Training \" + label)\n",
    "    plt.plot(np.arange(len(val_data)) + 1,\n",
    "             val_data, \"r.-\",\n",
    "             label=\"Validation \" + label)\n",
    "    plt.gca().xaxis.set_major_locator(mpl.ticker.MaxNLocator(integer=True))\n",
    "    plt.legend(fontsize=14)\n",
    "    plt.xlabel(\"Epochs\")\n",
    "    plt.ylabel(label)\n",
    "    plt.grid(True)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Baseline\n",
    "\n",
    "Before we begin modeling in Keras, let's create a benchmark using a simple heuristic. Let's see what kind of accuracy we would get on the validation set if we predict the majority class of the training set. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.29490392648287383"
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(yvalid == ytrain.value_counts().idxmax()) / yvalid.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok. So just naively guessing the most common outcome `UP` will give about 29.5% accuracy on the validation set. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linear model\n",
    "\n",
    "We'll start with a simple linear model, mapping our sequential input to a single fully dense layer. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(units=N_LABELS,\n",
    "                activation='softmax',\n",
    "                kernel_regularizer=tf.keras.regularizers.l1(l=0.1)))\n",
    "\n",
    "model.compile(optimizer=Adam(learning_rate=0.001),\n",
    "              loss='categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "history = model.fit(x=Xtrain.values,\n",
    "                    y=ytrain_categorical,\n",
    "                    batch_size=Xtrain.shape[0],\n",
    "                    validation_data=(Xvalid.values, yvalid_categorical),\n",
    "                    epochs=30,\n",
    "                    verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_curves(history.history['loss'],\n",
    "            history.history['val_loss'],\n",
    "            label='Loss')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RMWaTiGzN1G4hME1EThhjooDPgWHAG4764cA2EUkyxtwAfGeMqSEifzrqD4rIVV6/GyULa9fCbbdBsWJWudSoYctVyShKwcZnMxtjTCTQFXhKRBJEZBVWqfTJ3FZEdonICeelQApQLV39ZhFxZoIXoBBwtTflV3JnxQro0MFGV165Mk3RKIqi+NKMVgNIEpEd6co2AbVdNTbG9DTGnAKOAPWB9zPVf2WMSQTWArFA+qXncsaYv4wxexwmuEgP3ke+Ji7O7siPi8vbdUuW2BnN1VdbRVOlilfEUxQlSDGS3lXImwMZ0wqYKyIV0pUNBHqJSJscrqsO3Ae8LSKHMtUVAjoANUXkDUdZBSAK+BWoDHwExIvIYBd9DwIGAZQvX77RnDlzcr2PhIQEihUrlms7f5NZzk2bSvLTT1FUqXKG8uXPc/p0GKdPF3K82r/374/gl19Kk5ICYWHC889voXnzY7mOtXJlGZ5/vhZVqpzh1Vc3U6rUxUuSMRBRGT1HMMipMuadtm3brheRxrk2FBGfHEBD4GymspHAl25c2wOYl0P9EqBjNnXNgCO5jdGoUSNxh+XLl7vVzt845Tx1SqR/fxHrgOz6MEakdGl7ZK6rW1dk5EiRb74ROXs26zgzZ4qEhoo0ayZy/PilyRjIqIyeIxjkVBnzDvCzuKEDfGlG2wGEOWYqTuoDmZ0DXBEGRF9ivVAAXbyTkgzvvgvVqtmoyk5CQqBfPxtJedcuOHbMRlo+dsyG+o+IsG7KRYrAgw/aKMqTJtlUyqVLw803w6uvwscf282avXtbF+elS6FUKf/dr6IogY3PvNFE5IwxZh7wvDFmANYbrRNwY+a2jvqFIvK3MaYWMAb4xlF3HXANdp0mCegO3ASMdtS3BXYD+4CrgJeBBRQQRGD+fBgxogl//GEVwYsv2hD8zuCVAwfa/DCZyS7u2NmzNvHY0qX2GD067ZqQEBuxuXhxX9ydoijBiq9dnx8CYoC/gaPAgyKy1bGe87WIOA2RLYCXjDHFgMPAXOApR50BngVqAcnATqC7iPziqG8IzARKO8aYD/zHy/cVEKxeDY8/bhf3K1cWFi602S+NgTp13Ate6SruWNGidvH/ttvs+ZgxNtS/iO07Lk43ayqKkjM+VTYicgy4x0X5SqBYuvN+OfQRD9yQQ/0bpO3HydfExaVlrPzsM/jiC5tGecoUqFr1Z9q3b53a1pPBKzt2hLfe0gyYiqK4jwbiDELOnoV58+CBB2x2SxE7+3jhBXj0UYiMhNhY73kZaph/RVHyiiqbAGTNGli82M5Yiha1C/npjz//zNjeGBgxAsaO9Z2MGuZfUZS8oMomADh0yEZDXrcOvv3W9YbKihUhOtqum1StauOOjR9vPckKF7ZrM4qiKIGKKhsfEhdnZyxRUdYUtm6dVTIHDtj6kBDramyMNY2FhMCwYTBunHVJzswtt6gpS1GU4MAtZWOMeRP4UES2eFmefMuECTByZMbcLtdeaxVF48bQpAk0aACbN0P79mmL7926uVY0oKYsRVGCB3dnNk2AR4wx64EPgU9E5LT3xMo/HDhgF+3nzk0rCwmx6yvPPZe1vS6+K4qSH3FrZ72ItMDua1kOPAP8aYz52BjTOucrCy5JSdY9uGZNWLgQBgzIuDvfuWfFFc2b270sqmgURckvuB3GRUS2i8i/saH8e2D3xSw1xuw0xjzhyDujYHO6NGliPcRatICtW+GDD+yMRZOIKYpSELmUmGGFgBJASSAUGxamD7Avu8ybBYXjx2HIEKtIDh+2prPFi60XGeiMRVGUgovb3mjGmMZAf+ys5iw2dP8AEdnjqH8QmADM9oKcAUtcnE17nJgI771nA1qOGGHXYzRemKIoisVdb7T/A67FBsO8H1gkIsmZms3Fpn0uMMTFQbt2VtEA1K5t98nUr+9fuRRFUQINd2c2nwExInIguwYicoQCFso/Nta6KIP1MOvZUxVNQeDUqVP8/fffXLzoXpI4b1OyZEni4+P9LUauBIOcKmNGChUqRLly5ShRosRl9+WusnkFF4rEGBMOpIjIhcuWJAhp08Z6ljn3xLRt62+JFG9z6tQp/vrrLypWrEhERATGGH+LxOnTpykeBDbbYJBTZUxDRDh37hwHHLvOL1fhuDsTmYtND5CZIdhZT4HEuSdGPcwKDn///TcVK1akaNGiAaFoFMVbGGMoWrQoFStW5O+//77s/tyd2bTAdU6Yb4EnL1uKIEZ38RcsLl68SER2IR0UJR8SERHhEZOxuzObotismJlJAQJ7zqkoHkZnNEpBwlOfd3eVzWbgXy7KewIaL01RFEXJEXfNaM8DC4wx1YDvHWXtgX8Cnb0hmKIowUGPHj1ISkri888/d/uaZs2a0bJlS1577TUvSqYEEm4pGxFZbIy5GxgLTHQUbwA6isjX3hJOUZTLJzczSN++fZk+ffol9//+++8jkrfMsIsXL6ZQoUKXPGZe2b9/P1WrVqVSpUrs3LlTTaF+wO0IAiKyBFjiRVkURfECf6ZL7frVV18xcODADGXZOTxcvHjRLYVQsmTJPMsUFeXbUIoxMTF069aNlStXEhsbS1s/71O4cOEChQsX9qsMvqZAbcJUlIJIhQoVUo9SpUplKStZsiS//vorxhjmzp1L69atCQ8P56OPPuKvv/6ie/fuqe7ederUYdasWRn679GjB/fee2/qebNmzXj00Ud5/PHHiYqKokKFCowZMybD7KdZs2aMGjUqg4yvvPIK/fv3p3jx4lx99dVMnDgxwzjbtm2jRYsWhIeHU6tWLb799lvCwsKYM2dOjvcvIkybNo2+ffvSu3dvpk6dmqXN8ePHGTRoENHR0YSHh1O7dm3mzZuXWr9y5Upat25N0aJFKVWqFDfffDOHDx92eS/ZvSfDhw9n+PDhlClThvbt2wPwyiuvUKdOHSIjI7n66qt58MEHOXXqVIa+Mo/dqVMnDh8+zJQpU6hQoQJJSRl9t7p27Uq3bt1yfE/8gVvKxhhT2BjznDFmhzEm0RiTnP7wtpCKkh+Ji7OpvV2lAfcXTzzxBI8++ijx8fHccccdnDt3jmbNmrFo0SK2bNnCgw8+SN++fVm1alWO/cTExFCyZEnWrl3L66+/zn//+1+++uqrHK957bXXaNq0KRs2bEh9MP/yyy8AJCUl0alTJ4oXL85PP/3ElClTePLJJ0lJScn1nr777jvOnz9P+/bt6dOnD/PmzePEiROp9cnJydxyyy38+OOPfPDBB2zbto3//ve/qbO6devW0aFDB+rWrUtcXByrV6+mc+fOWR7yuRETE0PRokVZs2YNH3zwAQBhYWFMnjyZrVu38vHHH7NixQpGjhyZeo2rse+66y6SkpL417/+RUJCAosXL05tf+TIEb788kseeOCBPMnmC9w1o70AdAfGY4NtPg5UwQblfMorkilKkDBiBGzcmLdrTp60WVlTUmyoo3r1IC/WqAYN4M038zamOzz22GPcc889GcoeffTR1L8ffvhhvv32W+bMmUPLli2z7ef6669n7NixAFSvXp333nuP2NhYevbMPjD8XXfdxZAhQwAYNWoUb731Ft9//z3XX389ixYtYu/evaxevZpy5coBdlbgnCHkxNSpU+nZsychISFcd9111KlTh5kzZzJ06FDArh9t2LCB7du3U65cOYoXL07VqlVTr3/55Zdp3rw5kydPTi2rXbt2ruNm5rrrrmP8+PEZytIrlipVqjBu3Dj69OmTqoxcjV2pUqXUCAI9evQgJiaGjh07AjBjxgwqVKjAzTffnGf5vI27ZrRuwBAReR9IBhaIyDBsIrXAuytFCXBOnrSKBuzryZP+lcdJ48aNM5wnJSXx3HPPUbduXaKioihWrBiLFi1i3759OfZTr169DOdXXnklR44cyfM1zp3rv/76K1WqVElVNAA33HBDrvdz9OhRvvjiC/r06ZNa1qdPnwymtA0bNlC5cmWinblAMrFhwwa3lFpuZH5vAZYuXUq7du2oWLEixYsXp2fPniQkJHDs2DG3xh44cCCLFi1KfZ+mTZtGv379CAkJvBUSd2c25YFtjr8TgFKOv5dg46YpSoHlUmYYcXHQvn1aXL1ZswIjEkVkZGSG85deeom3336bN998k9q1axMZGcnIkSM5f/58jv1kdiwwxuRq8rqUa3JjxowZnD9/nkaNGmUoT05OZv369VnKL4WQkJAs3ngXL17M4vGW+b3duXMnd999N0OHDmXcuHFERUXx448/0rdvXy5ccC/c5A033EDNmjX5+OOPad26NVu2bGHhwoWXd0Newl31tw+40vH3b8Ctjr+bA+c8LZSi5HeCJa7eqlWr6Ny5Mz179qR+/fpUrVqVHTt2+FyO6667jr1796YuygP89NNPuV43depURo4cycaNGzMcHTp0SJ3dNGzYkL1797Jr1y6XfTRs2JBly5ZlO0bZsmUzePelpKSwefPmXGX76aefMMbw+uuv06xZM2rUqMH+/fvzNDbAgAEDmDZtGjExMbRr144qVarkOrY/cFfZzMdu4gR4C3jOGLMHmA586AW5FCXfEwyZW2vUqME333xDXFwc8fHxDB48mIMHD/pcjjvvvJNKlSrRt29fNm/ezOrVq3niiScwxmS7Z2bt2rVs2bKFQYMGUadOnQxH7969mT17NufOneOOO+6gfv36dOnSheXLl7Nnzx6++eabVIeGf//736xZs4ahQ4eyefNmfv31V95///1UBdOuXTsWLlzI4sWL2b59O8OGDXMrcGX16tU5f/48kydPZs+ePcyYMYN33nknQxtXY8fExGRQbn369GH37t3ExMQEpGOAE7eUjYiMEZGXHH9/DrQEJgFdRMRVgE5FUfIBzz33HPXq1ePmm2+mTZs2lCtXLoNLr68ICwtjwYIFnDhxgiZNmjBgwACefvppAMLDw11eM3XqVOrWrUuNGjWy1HXq1ImzZ88yd+5cQkNDWbp0KY0aNaJfv37UqlWLkSNHpnqbNW3alKVLl7JhwwaaNm1K8+bNmTdvHmFhdhViyJAh9OzZk969e9OqVSsqVKjA7bffnus9NW3alFdffZVx48ZRu3ZtZs6cySuvvJKlTeaxFy5cmDo2QOnSpencuTORkZF07hzAAV1EJMcDKAR8CkTn1jaYj0aNGok7LF++3K12/iYY5AxGGbdt2+YfQXLg1KlT/hbBLTwt548//iiAbNmyxWN9BsN76UrGNm3ayNChQ702Zk6fe+BnceMZm6uDgIhcNMbcAozxqtZTFEXJgblz51K6dGmqVavGrl27GDFiBE2bNr0kN+T8wrFjx/j+++9ZuXIlU6ZM8bc4OeKuN9o8oAugUfMURfELJ0+eZMyYMezfv59//OMftG/fnjfeeMPfYvmVWrVqkZiYyKuvvkr16tX9LU6OuKts9gFjjTGtgJ+BM+krRaRg/8cVRfE6AwYMYMCAAf4WI6A4dOiQv0VwG3eVzf3AcaCe40iPAKpsFEVRlGxxN8XANd4WRFEURcm/BF5MA0VRFCXf4dbMxhgzMad6sXHS3OknCpgK3AIcAcaIyGwX7R4FHgHKYMPjfAo8LiJJjvrlQB2gCLAHeFpEFqS7vic2aGgZ4Fugv4gcc0dGRVEUxfO4O7Opm+m4HugJ9ME+9N3lbeACNtZaL+BdY4wrv8WFwPUiUsLRf30gvUIbDlzhqB8EzDTGXAHg6O99h2zlgbNAxm25iqIoik9xd80mS1o7Y0w4dpay0p0+jDGRQFegjogkAKuMMQuxSuGJTOOlD1JkgBSgWrr69IGHBLvx9GrgT6wS+1JEfnCM+xQQb4wpLiKn3ZFVURRF8SyXvGYjIonAOMDdcDU1gCQRSR/FbxPgckeWMaanMeYU1txWHztbSV//lTEmEVgLxGJdsnH0tymdnLuws6msMSsURckTH374YWq2T1fnrnj55ZepVq1ajm0uZWwluHDX9Tk7ygDF3GxbDDiVqewkUNxVY8dazmxjTHXgPuCvTPV3GWMKAR2AmiLijEVezNFvruMYYwZhzXCUL1+e2NjYXG8iISHBrXb+JhjkDEYZS5YsyenTgTVBTk5OzlGm7t27c/bsWb788sssddu3b6dJkybMnz/frZwtiYmJAKnYejeZAAAgAElEQVTjdezYkXbt2uU4/vnz50lJSclVTidJSUlERUUxe/Zs7rrrrtRyd8a6XNLLeOjQIerUqcMVV1zB5s2bsw346WvcfR89SWJi4mV/V911EHgscxFwBdZktTjrFS5JAEpkKisB5PiuichOY8xW7LpLl0x1F4GvjTHDjTG/icjCvIwjIlOAKQCNGzeWNm3a5HoTsbGxuNPO3wSDnMEoY3x8fGqWxEDh9OnTOco0ePBgOnfuzNGjR7OEn58zZw6VK1emY8eObiXccga9dI5XvHjxDAnNXFGkSBFCQkIIDQ11671zBsCMiIjI0N6dsS6X9O/lpEmT6NKlC2vXrmXt2rV+z3554cIFChcunOv/2xuEh4fTsGHDy+rDXTPaI5mOh7CRn6cBQ9zsYwcQ5pipOKkPbHXj2jDAdRq9rPVbHf0CYIypivVa830SDkUJAO68807Kly/PtGnTMpRfvHiRGTNm0L9//1RFM2rUKGrUqEFERATXXHMNTzzxRI6J0lyZtsaPH0/58uUpXrw4999/P2fPns1Q73xwlylThhIlStCqVasMuWmcCrFz584YY1JNcK7Geuedd4iOjqZw4cJUr16dmJiY1LqkpCSMMXz44Yd07dqVyMhIoqOj+eSTT3J9z0SEmJgY7rvvviyZPZ2cOHGCwYMHU6FCBcLDw6lVqxaff/55av2aNWto27YtkZGRlCpVig4dOvDXX9ZA07JlS0aMGJGhv969e2dIyd2yZUuGDh3KY489RtmyZWndujUAb731FnXr1iUyMpKrrrqKwYMHczJTqtfsxo6JiaFcuXJcvHgxQ/vu3bvTpUuG3/Iex90UA9dkOqJFpJmIPOnuoruInMHGWHveGBNpjGkBdAJmZG5rjBlgjCnn+LsWNgjoMsf5dcaY240xEcaYQsaY3sBNwArH5bOAu40xrRxOCc8D89Q5QAk44uJg/Hj76kXCwsLo27cv06dPz5D58ssvv+TIkSP069cvtaxEiRJMnz6d+Ph4Jk+ezMyZM3n55ZfdHmv27Nk8++yzvPjii6xfv56qVavyZqZUpqdPn6Zv376sXLmStWvXUrduXW6//XaOHz8OwLp16wCb4vjPP//kxx9/dDnW3LlzGTFiBCNHjmTLli08/PDDDBo0iK+//jpDu+eee46uXbuyadMmunTpwv33358lSVlmYmNjOX36NDfffDN9+vThiy++4OjRo6n1KSkp3HbbbaxevZqPP/6Ybdu28dprr6VmG/3ll19o164d1113HWvWrGHNmjV07do1y0M+Nz766CPCwsJYtWpVqiINDQ1l4sSJbNmyhZkzZ7J69eoMiiunsbt3705iYmIGk+qxY8dYsGCB93PhuBMaGigMhLsoDwcKu9OHo30U8AU2tto+oKejvBWQkK7dNOwazRngd+BV5/hATaxTwGngBLAO6JxpnJ6O/s8AC4Co3GTTFAO+JxhldBlqffhwkdat83Y0aCASEiIC9rVBg7xdP3x46vDuhMXfsWOHAPLNN9+klt1xxx1y22235XjdpEmT5Nprr009/+CDD6RkyZLZnjdp0kSGDBmSoY/WrVtLdHR0tnKmpKRImTJl5JNPPhERkYsXLwog8+fPz9Au81hNmzaVgQMHZmjTq1cvad26dYZ+xo4dm1p//vx5KVy4cOpYmXHK2KtXLxme7j1u3ry5TJgwIfV88eLFEhISItu3b3fZT7du3aRFixYu60REWrRokaF/55idOnXK0KZhw4bZyujkyy+/lIiICElJSXFr7MGDB8udd96Zej5x4kS58sorJSkpKdtrPJFiwF0z2lys6SwzQ4DP3OwDETkmIveISKSIVBLHhk4RWSkixdK16yci5R3tqojI42K93xCReBG5QUSKi0gpEWkiIvMzjTPb0X+kiHQS3dCpBBonT4JzlpGSYs+9SPXq1WndunXqr+ODBw/yzTffZPk1++mnn9KiRQsqVKhAsWLFGDVqFPv27XN7nPj4eJpnSj2a+fyvv/5i0KBB1KhRg5IlS1K8eHGOHj2ap3GcY7Vo0SJDWcuWLdm2bVuGsnr10sI5Fi5cmDJlyuSYSfPEiRP873//o0+fPqllmU1pGzZs4KqrrnKZmM1Z747DRW40atQoS9ny5ctp3749FStWpHjx4nTr1o1z586lpszObeyBAweyZMmS1GyfMTEx3H///YSGhl62vDnhrjdaC1y7OH8LPOk5cRQlCMlkJnKLuDho3x4uXIDChWHWLK/nh37ggQcYOHAgx44dY/r06URFRdGpU6fU+lWrVtGrVy+ee+45brnlFkqVKsX8+fN58knPfsV79+7NiRMnePPNN6lcuTJFihShTZs2XLhwwSP9Z/Yac5q20tenNydmZubMmSQmJnLDDTdkKE9OTmbt2rVZyi+FkJAQpxUmFVcmtsjIyAznu3fvplu3bjz44IO89NJLREVFsW7dOnr37u32+9eoUSPq1q3LRx99xK233sqmTZsyrDV5C3dnNkWBJBflKWTjuqwoSg40bw7LlsELL9hXLysagHvvvZfw8HBmzpyZuvid/kG8evVqKleuzH/+8x+aNGlC9erV+f333/M0Rs2aNbOssWQ+X7VqFcOGDeOOO+6gdu3aREZGZgiVHxoaSmhoKMnJybmOtXr16ix916pVK08yZ2bq1KmMGDGCjRs3Zjhuu+221NlNw4YN2b9/Pzt2uPY7atiwIcuWLct2jLJly6bOLMAuZ2zatCnb9k7WrVtHSkoKr7/+Os2aNaNGjRocOHAgT2ODnd1MmzaNmJgYbrrpJqKjc/K/8gzuKpvNwL9clPcEtnhOHEUpQDRvDmPG+ETRgHUl7tmzJ88++yy7du3KYkKrUaMG+/bt45NPPmHXrl1MnjyZzz5z20oOwPDhw4mJiWHq1Kns2LEj1VEg8zgzZswgPj6en376iR49elCkSJHUemMMlSpVYtmyZRw6dCjVcSAzjz/+ONOnT+fdd99l586dvPnmm8yZM4fRo0fnSeb0bNiwgY0bNzJw4EDq1KmT4ejduzdz5szhzJkz3HrrrTRq1IguXbqwdOlS9uzZw9KlS1m4cCEAo0ePZt26dTz44INs3ryZ7du3M2XKlFTHhHbt2rFo0SK++uortm/fzogRIzIon+yoXr06Fy9eZOLEiezZs4dZs2YxadKkDG1yGxugV69e7N+/nylTpnjfMcCJOws7wB3ARayn1wOOY7aj7C53+gj0Qx0EfE8wypjTQqm/cMdBwMn69esFkBtvvNFl/eOPPy5lypSRYsWKSdeuXWXy5MkSGhqaWp+bg4CIyAsvvCBly5aVyMhI6d27t4wdOzaDg8Avv/wiTZo0kSJFikh0dLTMmjVLrr32WnnhhRdS+5g/f75Uq1ZNwsLCJDo6OtuxJk+eLFWrVpVChQpJtWrV5MMPP0yty87RoGLFihkW+9PzwAMPSM2aNV3WnTx5UooUKSJTp04VEZGjR49K//79pUyZMlKkSBGpVauWfP7556ntV6xYIS1atJDw8HApWbKkdOjQQQ4dOiQi1lFhyJAhEhUVJWXKlJFnn33WpYNAZicCEZFx48bJlVdeKeHh4dKhQweZPXu2APLHH3+4NbaTPn36SMmSJeXs2bMu7zc9nnAQcPthDNwGrMJ6eJ1x/H27u9cH+qHKxvcEo4zBrmz8STDIWZBk7NChQxbPwezwhLJxO1yNiCwBlnhmPqUoiqL4g2PHjrFixQq+//57Jk7MMXuMR3E3XE1rABFZ4aJcxBFhWVEURQls6tWrx+nTp3nllVeoWbOmz8Z1d2YzAbsTPzMlgGeBrM7giqIoSsCRW/QEb+GuN9q1pAvbn44tjjpFURRFyRZ3lc05bJTnzFTE5opRlAKDXRNVlIKBpz7v7iqbb4BXjDGlnQXGmChgvKNOUQoEhQoV4ty5c/4WQ1FyJiEB/vzTvl5m23PnzmWJwnApuLtmMwr4AfjdGONMyVwPOAx0v2wpFCVIKFeuHAcOHKBixYpEREQETEItRUklIQF27LAx94yBsmVtSCRXXLgAhw+DCISEQI0aUMyGqRQRzp07x4EDByhfvvxli+WWshGRP40x9bHJ0ho4ij/CbvJsARy8bEkUJQgoUcLm5Tt48GCew8V7i8TExNSkZoFMMMiZL2Q8eRJOnEg7dwTodIsLF6BkydTTQoUKUb58+dTP/eWQl302Z4EPAIwxFYF+WAeBKoB3w4UqSgBRokQJj3z5PEVsbOxlZ1H0BcEgZ76QMS4O7r4bkpIgPBy+/BKaNnXd9qefoGPHtICwy5aBl9yh3VY2xphQbLKzB4BbsPHS3semH1AURVECgebNoU0bWL8eFi3KOfZehw5WwcTG2mu8GKcvV2VjjLkWGADchw1TMxu4FegjIttyulZRFEXxA3//bRWHO8rD3XaXSY7eaMaYlcCPQGmgm4hUFZGxgPp+KoqiBCLJybB9u9fMYZdKbjOb5sDbwBQR2eoDeRRFUZTL4fff4fx5uMy8Pp4mt302TbAKaZUxZoMx5lFjTAUfyKUoiqJcCs602AE2s8lR2YjIBhF5GBs94A2gI/CH47o702/yVBRFUQKA+Hj7GkzKxomIJIrIDBFpC9QEXgUeBQ4ZY772poCKoihKHoiPhwoVoFQpf0uSAXfD1aQiIr+JyBPA1UA3NDaaoihK4BAfH3DrNXAJysaJiCSLyAIR6eRJgRRFUZRLRMSu2QSYCQ0uQ9koiqIoAcbBg3D6tCobRVEUxYs4nQPykxlNURRFCTAC1BMNVNkoiqLkH7Zts15oHkgJ4GlU2SiKouQX4uPtrCYA8yypslEURckvBKjbM6iyURRFyR8cPWqjPQfgeg2oslEURckfBLBzAKiyURRFyR8EsNszqLJRFEXJH8THQ9GiUKmSvyVxiSobRVGU/MC2bXDttRASmI/1XNNCK0qBZflyqk2eDH/8AfXrZ99u0yb7q/Luu32SXldRXBIfDy1b+luKbFFloyiuiIuDDh24KiUF5s1z75o33oDly1XhKL4nIQH27QvY9RrwsRnNGBNljJlvjDljjNlrjOmZTbtHjTG7jTGnjDEHjTETjDFhjrpyxphPHOUnjTGrjTE3pLu2jTEmxRiTkO7o66t7VPIJixZBSor9OyQEevWC//0v69GrV5rZ4vx5+P57/8msFFy2b7evAeqJBr6f2byNzX9THmgALDLGbBKRrZnaLQSmicgJY0wU8DkwDJsttBiwDngM+Bt4wNFPFRFJcFx/UESu8v7tKPmWxEQAJCQEU6QIPPyw6xnLFVfYmc/581Y5nTrlY0EVhYBNBZ0en81sjDGRQFfgKRFJEJFVWKXSJ3NbEdklIieclwIpQDVH3W4ReUNE/nTk1JkCFAau9cmNKAWDuDioWpU9/fvDsmXZm8aaN7f1L7wADRrA1Klw7JhvZVWU+HgIC4Nq1fwtSbYYEfHNQMY0BFaLSNF0ZaOA1iJyt4v2PYH3gOLAEaCDiGxy0a4B8CNQXkROGmPaAEuB48BZ4AtgrIiccXHtIGAQQPny5RvNmTMn1/tISEigWLFiud+wnwkGOQNVxvA//6RZz57sHjiQbR07ui1j5K5dNB40iIN3383OESO8LGUa7r6PJbZupdTGjZxo0IBTtWv7QLKMBOr/Oz3BKmPtp56i6B9/sG76dJ/L07Zt2/Ui0jjXhiLikwNoBRzKVDYQiM3luurAC0AFF3UlgP8DxqQrqwDUws7argF+AN7PTb5GjRqJOyxfvtytdv4mGOQMWBlfekkERH7/Pe8yDh0qEhIisnGjV0RzhVsyrlwpUqSISGioSESEyJo1XpcrMwH7/05H0Mp47bUiXbr4XBYREeBncUMH+NJBIMGhHNJTAjid00UishPYCryTvtwYEwF8CfwoIuPTtT8kIttEJEVE9gCjseY7RckdEZg1C1q1gsqV8379889DVBQMHWr7CgSWLYN//tOuKyUnw4ULEBvrb6kUT3HhAvz2W0Cv14BvvdF2AGHGmOrpyupjFUluhAHRzhNjTBGseWw/MDiXawXdvKq4y6ZNdrG1p0tHydwpXRrGj4dVq2D2bM/Klle2b4eOHaFDB3vuDDtfuDC0aeM3sRQPs3On/RERwG7P4MOHsNg1k3nA88aYSGNMC6ATMCNzW2PMAGNMOcfftYAxwDLHeSGsd9o5oK+IpGS6tq0xprKxXA28DCzw4q0p+YlZs+xC6z//eel99O8PjRvD44/bfPC+5uhRGDYM6tSBFSvglVdgzx54911bn51nnRKcBHgATie+/sX/EBCBdVn+BHhQRLYaY1oZYxLStWsB/J8x5gyw2HE86ai7EbgLuAU4kW4vTStHfUNgDXDG8fp/WLdpRcmZ5GT45BO4/Xb4xz8uvZ+QEJg8Gf78E1580XPy5caFCzBhgvVIevttGDjQ/uodPRrCw2HQIGjaFBYsSNtDpAQ/27bZWeu1ge2Q69N9NiJyDLjHRflK7P4Z53m/HPpYgXWHzq7+Dex+HEXJGz/8AAcOwOuvX35fN9wA/frZh3///t57EMTFUWnWLPjxR+t2/dtvcNtt8NprkNnjzBh49FH4179g8WK46y7vyKT4lvh4u75YtGjubf2IhqtRFCezZ0OxYjbGmScYP95GGRg+HL7+2vOpeuPioF07rnFsQKVKFTvObbdlf03XrnD11Ta0jiqb/EEAZ+dMjy6cKwpYT63PP4fOnT33C7F8eeud9s03sHChZ/pMz6JFkJhop/khITBgQM6KBqBQIXjkERvDbeNGz8uk+JbkZOsIEuDrNaDKRlEsixfDiRM21pkneegha84aMQLOnfNcv2fPwvz5gA2pQ5Ei0K6de9cOHAiRkdbEpwQ3v/9uQyupslGUIGHWLChXDtq392y/hQrBpEn2ofDqq57pMykJevSAX3+F8eNzD6mTmVKl7DrSJ59YJwYleAnw7JzpUWWjKCdPwldf2Qd4mBeWMdu2hW7d7BrO779fXl8idsPol19aJfbEE+zr1SvvrszDh1ul9fbblyeP4l+CxO0ZVNkoil3EP3/e8ya09Lz2ml1Xuf9+q3Ti4i6tn3Hj4P334YknrInuUomOhk6d7N6bs2cvvR/Fv8THQ4UKdrYa4KiyUZTZs+3elCZNvDfG1VdDnz52k+V//mPNdXlVOB99BGPHQu/eVulcLo89ZiNUz8iyr1oJFrZtC4pZDaiyUQo6Bw/ahGc9e3reNTkzFSvaVxHrLPDhh+5fu3Sp9Tbr0MHup/GErC1bQqNG1lFAN3kGHyJB4/YMqmyUgs6cOfZL600TmpMOHSAiwprTjIGYGLvXxWl3z45ffrH7Y2rXtia/woU9I48xdnazfTssWeKZPhXf8eefNlmfzmwUJQiYNcvGMatRw/tjOROtvfii3efy6quwciXUrWsX/Y8cyXrN77/DnXfaSNKLF0OJzIHTL5N//tPOuN7QoBtBRxBk50yPKhul4PLrr3bW4ItZjZPmzWHMGGjdGkaNsuFlBg+G996z60avv26dFcAG1LztNruP4uuv4corPS+Pc5PnsmWwebPn+1e8RxC5PYMqm/yJM17WpXo8FRRmzbImrR49/CdD2bLW/XjzZrjxRquAatWyaaavvx5277bRB7z5QBk0yEZN0E2ewUV8vPVCK1/e35K4hSqb/MT58za0fIsWXDN16qV5PBUURKwXWvv21nXU39SqZc1kS5ZY2Z5+Gvbts+sq3tj7k57SpW3Q0Nmz4dAh746leI74eGtC87Zji4dQZZMfEIF58+wC8qRJIIJxejy9+WbgZIwMJNautbOGS02S5i1uvRUeeCDtAZKc7JusmsOHw8WL8M47ubdVAoMgcnsGVTbBz/r1Nuti1642Z8mECRARYeNlGQOffWbXCdas8bekgcWsWfb96tLF35JkpV07K1toqO+yalavbqNdv/uuZ2O4Kd7h2DH4+++gWa8BVTbBy4ED0Lev9aT69Ve7q3zjRhvwcdkyGy9r5UqYPh3++ANatIDu3W3GxoLOxYvw6af24epp7y5P4PRae+GFvMU8u1wee8x6xM2c6ZvxlEsniMLUOFFlE2ycOQPPPmt/iX76qQ1bsnOnXeR12vabN7fxslq0sAppxw545hkbT+u66+Df/7bxwAoq330Hhw/71gstrzi91nyZvvmmm6BhQzs7VtNrYBOEykaTpwULq1fb9ZfYWPvrs3t3G2PrmmtyvzYy0iqogQNtqJT//hemTbNxukJDrRdU48bZX//zz3bW1KFD/shd/+ab1kxVurS/JQksnJs8+/SxUaEHDcof/+9gYMUK+x1v29a993zbNrtBuHJl78vmIVTZBANxcdZun5RkXXXff98+CPJKxYrWrPbIIzb0SV5D3o8bZ0O7BPMDaMYMG/rFGLuHxZdmqmCgUiX7On26nTnr++NdEhNh5Mg0x4yICPfe8/h4a6UICR7jlCqbYOCbb6yiAfuQPHr08vpr1MjuHN+82cbECgmBjh1dZ3lcsgQWLLBmlcREmx0yWB8++/bBww/bv0XgwgU7UwzW+/EGq1fbz5iIdaXX98c7iMDcudaknT7tRGKijS7hjrJp0cKrInoaVTbBgHN9xZPeSW3b2uyOFy7YPkePdv0Br1fPKrvz561iWrAAnnzSc6mTfcXx43D77daVODzcOgn4ytMrmGjTxr4/587ZB2LTpv6WKP+xdq01V65ZY79fEydapZOYaN/zP/7I8fLQc+dg715rnQgiVNkEOsnJ9gFfty7861/2YeCJX5pOj6fY2Jz7TN9OxIa479ULPv/cKr9gIDER7rnHhoZZssQ+THO774KK8//98cc2hM5333k+e2lBZd8+6/Qxe7bd9f/hh2nrpo0b2xnNihX2fb/xRrt25oKIffvsH0Hk9gyqbAKfhQutu/Lnn9u9NJ6keXP3Hrbp2xUrZjcADhsGkycH/u7llBTrkffDDzYNctu2tlyVTPY4/9/nztlYbf36+SZQaX4kLs5aBvbssXvewDrp/PvfULx4Wjvnez5qlJ2B9+9vI1vcfHOWLiOdyiaIPNFAlU3g88YbUKWK/WUeCAwbBvv3W+eCq6+2rteBzKhR9kv+6qv+jYEWjLz8Msyfb/duLVoU+D8sAo01a+zs+eJFe37LLfDBB2lOGK4oXNhGA7npJvvj8ocfoEGDDE2K7t1rtzlUq+Y92b1A8LgyFETWrYNVq+xMIpBMVi+/bE16Y8YEdpbHCRPsMWyY9fhR8kaFCtZl/uuv4auv/C1N8PHkk2mKJjTUKp6cFI2TkiVtnLxSpewsJ70DAQ5lU62ajdgdRKiyCWQmTLA73Pv397ckGQkJsft02ra1sn33nb8lyspnn9lF2K5d7exQf5VfGkOH2rWBESPs2pfiHu+8Y9dfQkMvzbGnYkWr5BMTrcI5diy1KnLv3qBbrwFVNoHLH39Y18gBAwIzpEqRItbEUrOmjS+2caO/JUpjxQq7uNqypQ29EkizwmCjUCHrLbV7N7z2mr+lCQ4WLLB72e6+2y76X2rYodq14Ysv7HvfsaNdQ7twgYgDB4JuvQZU2QQukyfbxe1hw/wtSfY4p/slS8Idd1h3TH+zdatd34qOtl/68HB/SxT8tG8P995rN/U6F6cV18TF2bXBxo2tQ0qrVpcXdqh1a2uqXr0aeveG7dsxKSmqbBQPkZAAU6ZYE1Cgh6O46irrTnz2rP1iPPWUf3LoxMVZZ4W2be0u7K+/tqmUFc/w+uv2ddQo/8oRyOzYYWczV11l17giIz3Tb7du1hQ8b15alHLnWlAQocomEJk+HU6csGsOwUDt2vZX79698OKLNkS+LxVOXJwd85VXbIDN8eMDX0kHG5Uq2QXvuXOtSUjJyKFDNgJHSIj98VW2rGf7f/RRGw/xt98QgIceCrrEiKpsAo3kZHjrLWjWzB7BwsmTaXGaEhOt0rlwwfvjnj1rZ1POxeuQEDh40PvjFkRGjYKqVa1pNwh/WXuNhAS46y746y/rIh4d7Z1x6tUDwEBaqKUgQpVNoPHVV3ane7DMapy0aWOdBkJD7QN/8eK0BU5vhKtPSbG27Bo17C9tp9dPkSIagsZbOJPzbdsGb7/tb2kCg4sXbZzBjRutB2STJt4by2EiTgkJCcpQS6psAo033rAmoM6d/S1J3kif8GvVKrtmUriwvY927eCXXzw31sqVcMMNcN99cMUV9nzlSt8nGyuI3H23dcV95hn7S74gIwKDB1uz2XvvwZ13enc8x3fs9/79g/JzrhEEAon16+2O4ddfT0uEFkxkDn/ToYPdMf3009Y7p29feOkluPLKS+t/1y4b5uN//7OLsDNmQM+eaea7IPvyBSXG2HxAdepYh4xp0/wtkf949ll7/08/7bugmM2bs+/8eaoG4WddZzaBxIQJNl7SAw/4WxLPEBYGDz5ozYKjRtkAhNWrw/PPw/ffU2nWrNwXOePi7Je5Vy+7ke3rr+3127dbV9AgyueRb6hRw5p5p0+3/19PLlTHxVkHD3/0GRfn/meySxf7Oezf3yodJVd8+vPZGBMFTAVuAY4AY0Rktot2jwKPAGWABOBT4HERSTLGlAPeAloDkcAW4DERWZvu+p7AeMf13wL9ReQYgcyBAzZZ1dChdt9KfqJkSZsddMgQOzN55hkwhmtE7C/Ddu2gTJms1x05YpO1JSfb8zvvtC7hlzozUjzHzTdb77/33rNKxxNJ9b77zu7XSkqy60OeMBXNmGEDiSYn2zW9XD5r1yQnu/+ZDAmxUZs1OoVb+NpW8zZwASgPNAAWGWM2icjWTO0WAtNE5IRDQX0ODAPeAIoB64DHgL+BBxz9VBGRBGNMbeB94E7gF2AK8A4Q2FEYg2ET5+VStap1nR08GKZMsV41ycl2PcfVnphjx9IUTWioTRaliiYw+Okn+7BNSbGegE8+aSOUp49k7C7JyTB1qp0tOb3czp2zIV+aNbu0h/mhQ9ZLcerUNAYYvvoAAA1SSURBVAcVNz5refpMGmPXJ1u1yrt8BRER8cmBnYVcAGqkK5sBvJzLdf8AvgPeyaHNKaCR4+9xwOx0ddGOcYvnNE6jRo3EHZYvX+5WuzyRkCBSurRI164e69IrcnqKNWtEIiIkOSREJCLCnufQTkJDc27nRQL6fXTgFxnT/29CQ0VApEIFkQ8/FElKcnmJSzmXLhWpW9deX6+eSJEiIiEhIsbYsjZtRH75xX25zp4VeeklkWLFRMLCRLp1c+8zpJ/JSwb4WdzQAb40eNcAkkRkR7qyTUBtV42NMT2NMaew5rb62NmKq3YNgMLAb46i2o5+ARCRXTiU3OXegNf46CObSTLY3J0vFXe9atJ7uAWh902+Jv3/ZuVK+PFHO3MdMACuvz73jZ/x8dYsesstdp/K3LnWfXj5crtHa8UK6169ZYtNY96vX877p0RseJjrrrP5Yjp0sKGLPv3Uvc+Qfia9jhFv7IFwNZAxrYC5IlIhXdlAoJeItMnhuurAfcDbInIoU10JYDV2JjPeUbbMMc576dodcIwTm+n6QcAggPLlyzeaM2dOrveRkJBAsWLFcm3nNikpNO3bl6Tixfnl7bc9Zv/1uJxeQGX0DAEjowhlY2OpOmUKEYcOcaR5c3YPGcJZR1j9hIQESicnU2X6dK5cuJDkiAj29unD/s6dkcKFXXYZlpBApZkzuWrePCQ0lH09evBHt26kRESktimxZQvV3nmHEvHxnK5enV0PPcSJTDlg3CVg3sscCDQZ27Ztu15EGufa0J3pjycOoCFwNlPZSOBLN67tAczLVBYBrAA+yFS+ABidqew0DjNbdofHzWhr1oiMG5f7NPu//7Xmguefd69fNwm0qbYrVEbPEHAynjsn8vLLIsWLW1PWI4+ILFggR5o2FYmMtCaohx4S+ftv9/v87TeRe++135WKFUWeekpk1CiRdu1s2RVXiEyblq0Jz10C7r10QaDJiJtmNF86COwAwowx1UVkp6OsPpDZOcAVYdi1FwCMMUWAL4D9wOBMbbc6+nW2rQoUcYzvGz77zEZ+FbEzlcqVoWjRrO3Onk1LjDR+vJ3667RcCXbCw63XYb9+1vNw8mSYNIl/gHUqcO6PygvR0dbUtmoVDBxozVhO+vWzaRAC6Ne+khWfrdmIyBlgHvC8MSbSGNMC6IR1EsiAMWaAw8UZY0wtYAywzHFeCOuddg7oKyIpmS6fBdxtjGlljIkEnsfOik576daysn59mgeMiP0S1KqV9Uj/5QjCWEeKkiPlysG771oPS6d52JjLS0XRsqXNVeTcXxUaavduqaIJeHzt+vwQEIN1WT4KPCgiWx3rOV+LiPMT0wJ4yRhTDDgMzAWectTdCNyFVTYnTNoax+0istLR3xCs0nF6svXz/q2l4557YNIkq0AKF7Z7Q1zNWOLibK4QZ7sgi3WkKG7RvTtMmULK+fOEeOJz3ratjYGn35ugwqfKRuzGyntclK/E7p9xnmerHERkBY7Apzm0mQ1k2SzqM5weK7Gx9ouQm2dLbu0UJZhxenrFxFC1f//L/5zr9yYoCcIAXEFC5jhhl9tOUYIZT8f00u9N0KGBpRRFURSvo8pGURRF8TqqbBRFURSvo8pGURRF8TqqbBRFURSvo8pGURRF8To+C8QZ6BhjDgPubG0ug41EHegEg5wqo2cIBhkhOORUGfNOZREpm1sjVTZ5xBjzs7gT4dTPBIOcKqNnCAYZITjkVBm9h5rRFEVRFK+jykZRFEXxOqps8s4UfwvgJsEgp8roGYJBRggOOVVGL6FrNoqiKIrX0ZmNoiiK4nVU2SiKoiheR5WNmxhjoowx840xZ4wxe40xecxr6zuMMdWNMYnGmJn+lsUVxpgqxpjFxpjjxphDxpjJxhi/prswxgw1xvxsjDlvjJmerryZMeZbY8wxY8xhY8xcY8wVgSSjo66oMeYdY8wRY8xJY8wPfpKxiDFmquM7ctoYs9EYc3u6+vbGmF+NMWeNMcuNMZUDTcZ07Z42xogxpkOgyWiM6WaMiXfUbTPGZMkTFmiosnGft4ELQHmgF/CuMaa2f0XKlreBdf4WIgfewWZrvQJoALTGZnH1JweBF7GZZNNTGrsgWwWoDJwGpvlUsjSykxGsjFFATcfroz6UKz1hwB/Y/2lJYCzwmeMHRhlsavinHDL+DHwaSDI6GxhjooF/An/6QT7I+X2sCMwEHgNKAI8Ds40x5fwkq1uog4AbGGMigeNAHRHZ4SibARwQkSf8KlwmjDE9gC7ANqCaiPT2s0hZMMbEAyNFZLHj/FWghIgM9q9kYIx5EbhKRO7Ppv56YIWIFPepYBllyCCjMeY64CdH2Sl/yZUdxpjNwHPYNO33i8iNjvJI7E74hiLyqx9FTJVRRP7nOF8CTMT+MBogIt/5Uz7I8D7uB74UkXLp6g4DHUUkzl/y5YbObNyjBpDkVDQONgEBNbMxxpQAnsf+4glk3gR6OEw/FYHb/7+9+w+1u67jOP58uWmjaan/KGkW2Ihq2gQ1dRiTTQX7IXodgRnWHyqJOKxg+M91ZkpKooSiicp+aFDZMmHZYJkpaDILsuaVWk5Na7Yp/prbneW7P96fO09fzt09V8/3fr7I6wFf+N7v99xzXvdsn/M538/3w+cN/LpypkF9HthUO0TD8eRSS1eWYbQ/SxqpHQpA0iFk+9lEtpc/TZyLiB3A36ncjhoZkbQUGJ/4MtQFjYyPA2OSvixpVhlCGweeqJlxKi4LPZj9geY3xleBat9uJ3EVcEdEPC+pdpa9eQi4kHxPZwGrgHurJhqApKOBUeDM2lkaDgfmAz8HPgKcCKyT9GREjNUKJWlf4G5gVUQ8JWl/YFvjYVXbUZ+MBwDXAKfWytTUzFiOrQZ+DMwhh/eXls67s3xlM5g3yLHRXh8ix+87QdICYAlwQ+0seyNpH/IqZi0wl1xU8CDg2pq5piLpE8D9wLKIeLh2noadwFvA9yJid0T8DvgtcFqtQOXfeQ35QXhJOdypdjRJxhXAmoh4pkampn4Zy4SF64BFwH7kfZ3by2dAZ7mzGcxfgdmS5vUc+yzdGk5ZRN7Efk7SVuA7wIikP9YM1cfBwBHATRExHhEvkTfcz6gba3JlxtQG4KqIWFM7Tx/9hk+q3YxVXlbfQU6mGYmIt8qpTWS7mXjcXOBIKrSjvWRcDFxaZkluBT5K3phf3qGMC4CHIuLxiHg7IjYCj5FfNjvLnc0AyuXpWuC7kuZKWkgOpXTpg+c2suEuKNutwDrg9JqhmiJiO7AF+Kak2ZIOBM6n8nhzyTKHHNabJWlOOXYY8ADZOd7axYzksORzwOXlMQuBU4D1laLeQs6K+1JE7Ow5/gtgvqSR8neMAk9UmhwwWcbF5JDkRDv6J3AROcNzpk2WcSNw8sSVjKRjgJPp+D0bIsLbABv5jfxeYAfZsM+tnWmKvCuAu2rnmCTbAuBBcobfduCnwCEdeL+isa0Arij7b/RuXcpYzn0GeLT8/3wSOKtSxo+VXLsa79lXy/klwFPk0N+DwMe7lrHx2GeAJV3LSA6pbSaHIJ8mZ3fO+L/3dDZPfTYzs9Z5GM3MzFrnzsbMzFrnzsbMzFrnzsbMzFrnzsbMzFrnzsbMzFrnzsbsfaLUXjmndg6zftzZmA2BpJXlw765/b52NrMu8KrPZsOzAfha49juGkHMusZXNmbDMx4RWxvby7BniOsSSetKSeRnJf1fYTtJR0naIGmnsgz1Skkfbjzm/FKvZlzSi5JWNTIcrCxdvUPS031eY7S89nhZbHJ1K++EWYM7G7OZcyVwH7k23G3AaknHwp4VkNeT618dD5wFnERPCWhJFwE/IlfJPppcKfsvjdcYBX5Jrq78E+BOSUeU3x8hVwO/GJgHfJGs8GnWOq+NZjYEklYC55ELJ/a6OSKWSwrg9oi4oOd3NgBbI+I8SRcAPyBLO79ezi8i69LMi4jNkp4nF1ftW4q8vMb3I+Ly8vNsskDdhRFxl6RvkSsYz493lqs3mxG+Z2M2PBMVSHu90rPfrA//KPCFsv8pcrn93kJijwBvA5+W9BpwGPCbKTLsWWY+Iv5TatNP1Kr/GbAM2CJpPVnE7r6IGJ/iOc3eMw+jmQ3PmxGxubFtH8LzTmf4oXnFEpR2HhH/AD5JXt28BlwP/KEM4Zm1yp2N2cw5oc/PY2V/DDhK0gE9508i2+hYRPwbeIEs7vWuRcSuiFgXEZcBx5F1cBa+l+c0G4SH0cyG5wOSDm0c+29EbCv7Z0vaSBYNO4fsOD5Xzt1NTiBYLWkUOIicDLA2IjaXx1wN3CDpRbIK6weBxRFx/SDhJH2dbPOPkRMRvkJeCf1tmn+n2bS5szEbniXAvxrHXgAOL/srgBHgh8A24BuR9eOJiDclnQ7cSM4Q20XOKls28UQRcYuk3cC3gWuBl4FfTSPfK8ByciLCvmRFz7MjYss0nsPsXfFsNLMZUGaKLY2Ie2pnMavB92zMzKx17mzMzKx1HkYzM7PW+crGzMxa587GzMxa587GzMxa587GzMxa587GzMxa587GzMxa9z9NefxpWlbzFQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_curves(history.history['accuracy'],\n",
    "            history.history['val_accuracy'],\n",
    "            label='Accuracy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The accuracy seems to level out pretty quickly. To report the accuracy, we'll average the accuracy on the validation set across the last few epochs of training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3241512"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(history.history['val_accuracy'][-5:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Deep Neural Network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The linear model is an improvement on our naive benchmark. Perhaps we can do better with a more complicated model. Next, we'll create a deep neural network with Keras. We'll experiment with a two layer DNN here but feel free to try a more complex model or add any other additional techniques to try an improve your performance. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "dnn_hidden_units = [16, 8]\n",
    "\n",
    "model = Sequential()\n",
    "for layer in dnn_hidden_units:\n",
    "    model.add(Dense(units=layer,\n",
    "                    activation=\"relu\"))\n",
    "\n",
    "model.add(Dense(units=N_LABELS,\n",
    "                activation=\"softmax\",\n",
    "                kernel_regularizer=tf.keras.regularizers.l1(l=0.1)))\n",
    "\n",
    "model.compile(optimizer=Adam(learning_rate=0.001),\n",
    "              loss='categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "history = model.fit(x=Xtrain.values,\n",
    "                    y=ytrain_categorical,\n",
    "                    batch_size=Xtrain.shape[0],\n",
    "                    validation_data=(Xvalid.values, yvalid_categorical),\n",
    "                    epochs=10,\n",
    "                    verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_curves(history.history['loss'],\n",
    "            history.history['val_loss'],\n",
    "            label='Loss')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_curves(history.history['accuracy'],\n",
    "            history.history['val_accuracy'],\n",
    "            label='Accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3669173"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(history.history['val_accuracy'][-5:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convolutional Neural Network\n",
    "\n",
    "The DNN does slightly better. Let's see how a convolutional neural network performs. \n",
    "\n",
    "A 1-dimensional convolutional can be useful for extracting features from sequential data or deriving features from shorter, fixed-length segments of the data set. Check out the documentation for how to implement a [Conv1d in Tensorflow](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Conv1D). Max pooling is a downsampling strategy commonly used in conjunction with convolutional neural networks. Next, we'll build a CNN model in Keras using the `Conv1D` to create convolution layers and `MaxPool1D` to perform max pooling before passing to a fully connected dense layer. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "\n",
    "# Convolutional layer\n",
    "model.add(Reshape(target_shape=[N_TIME_STEPS, 1]))\n",
    "model.add(Conv1D(filters=5,\n",
    "                 kernel_size=5,\n",
    "                 strides=2,\n",
    "                 padding=\"valid\",\n",
    "                 input_shape=[None, 1]))\n",
    "model.add(MaxPool1D(pool_size=2,\n",
    "                    strides=None,\n",
    "                    padding='valid'))\n",
    "\n",
    "\n",
    "# Flatten the result and pass through DNN.\n",
    "model.add(tf.keras.layers.Flatten())\n",
    "model.add(Dense(units=N_TIME_STEPS//4,\n",
    "                activation=\"relu\"))\n",
    "\n",
    "model.add(Dense(units=N_LABELS, \n",
    "                activation=\"softmax\",\n",
    "                kernel_regularizer=tf.keras.regularizers.l1(l=0.1)))\n",
    "\n",
    "model.compile(optimizer=Adam(learning_rate=0.01),\n",
    "              loss='categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "history = model.fit(x=Xtrain.values,\n",
    "                    y=ytrain_categorical,\n",
    "                    batch_size=Xtrain.shape[0],\n",
    "                    validation_data=(Xvalid.values, yvalid_categorical),\n",
    "                    epochs=10,\n",
    "                    verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_curves(history.history['loss'],\n",
    "            history.history['val_loss'],\n",
    "            label='Loss')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_curves(history.history['accuracy'],\n",
    "            history.history['val_accuracy'],\n",
    "            label='Accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.34569758"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(history.history['val_accuracy'][-5:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Recurrent Neural Network\n",
    "\n",
    "RNNs are particularly well-suited for learning sequential data. They retain state information from one iteration to the next by feeding the output from one cell as input for the next step. In the cell below, we'll build a RNN model in Keras. The final state of the RNN is captured and then passed through a fully connected layer to produce a prediction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "\n",
    "# Reshape inputs to pass through RNN layer.\n",
    "model.add(Reshape(target_shape=[N_TIME_STEPS, 1]))\n",
    "model.add(LSTM(N_TIME_STEPS // 8,\n",
    "               activation='relu',\n",
    "               return_sequences=False))\n",
    "\n",
    "model.add(Dense(units=N_LABELS,\n",
    "                activation='softmax',\n",
    "                kernel_regularizer=tf.keras.regularizers.l1(l=0.1)))\n",
    "\n",
    "# Create the model.\n",
    "model.compile(optimizer=Adam(learning_rate=0.001),\n",
    "              loss='categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "history = model.fit(x=Xtrain.values,\n",
    "                    y=ytrain_categorical,\n",
    "                    batch_size=Xtrain.shape[0],\n",
    "                    validation_data=(Xvalid.values, yvalid_categorical),\n",
    "                    epochs=40,\n",
    "                    verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_curves(history.history['loss'],\n",
    "            history.history['val_loss'],\n",
    "            label='Loss')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_curves(history.history['accuracy'],\n",
    "            history.history['val_accuracy'],\n",
    "            label='Accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.35045946"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(history.history['val_accuracy'][-5:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multi-layer RNN\n",
    "\n",
    "Next, we'll build multi-layer RNN. Just as multiple layers of a deep neural network allow for more complicated features to be learned during training, additional RNN layers can potentially learn complex features in sequential data. For a multi-layer RNN the output of the first RNN layer is fed as the input into the next RNN layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [],
   "source": [
    "rnn_hidden_units = [N_TIME_STEPS // 16,\n",
    "                    N_TIME_STEPS // 32]\n",
    "\n",
    "model = Sequential()\n",
    "\n",
    "# Reshape inputs to pass through RNN layer.\n",
    "model.add(Reshape(target_shape=[N_TIME_STEPS, 1]))\n",
    "\n",
    "for layer in rnn_hidden_units[:-1]:\n",
    "    model.add(GRU(units=layer,\n",
    "                  activation='relu',\n",
    "                  return_sequences=True))\n",
    "\n",
    "model.add(GRU(units=rnn_hidden_units[-1],\n",
    "              return_sequences=False))\n",
    "model.add(Dense(units=N_LABELS,\n",
    "                activation=\"softmax\",\n",
    "                kernel_regularizer=tf.keras.regularizers.l1(l=0.1)))\n",
    "\n",
    "model.compile(optimizer=Adam(learning_rate=0.001),\n",
    "              loss='categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "history = model.fit(x=Xtrain.values,\n",
    "                    y=ytrain_categorical,\n",
    "                    batch_size=Xtrain.shape[0],\n",
    "                    validation_data=(Xvalid.values, yvalid_categorical),\n",
    "                    epochs=50,\n",
    "                    verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_curves(history.history['loss'],\n",
    "            history.history['val_loss'],\n",
    "            label='Loss')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_curves(history.history['accuracy'],\n",
    "            history.history['val_accuracy'],\n",
    "            label='Accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.36363825"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(history.history['val_accuracy'][-5:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright 2020 Google Inc. Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
